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Giant fight: Customer churn prediction in traditional broadcast industry

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  • Li, Yixin
  • Hou, Bingzhang
  • Wu, Yue
  • Zhao, Donglai
  • Xie, Aoran
  • Zou, Peng

Abstract

The national broadcast service providers in China have been involved in a fierce battle with various new providers from each node of the supply chain. Although there are historical advantages, customer churn rate has been increasing in recent years. To better respond to market competition, national broadcast service providers such as cable network enterprises should recognize customer preference and forecast customer churn intention before the competition does. Based on a positivist approach, customer churn is related to watching intensity, consumption amount, and paying habits. Watching preference (as the exclusive resource) has only a moderate effect on customer watching intensity and customer churn. In order to explore the mechanism between variables and observe user retention strategies, in-depth interviews were conducted. This study provides references to similar major traditional enterprises in the new competition era.

Suggested Citation

  • Li, Yixin & Hou, Bingzhang & Wu, Yue & Zhao, Donglai & Xie, Aoran & Zou, Peng, 2021. "Giant fight: Customer churn prediction in traditional broadcast industry," Journal of Business Research, Elsevier, vol. 131(C), pages 630-639.
    • Handle: RePEc:eee:jbrese:v:131:y:2021:i:c:p:630-639
    DOI: 10.1016/j.jbusres.2021.01.022
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    References listed on IDEAS

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    Cited by:

    1. Chen, Yan & Zhang, Lei & Zhao, Yulu & Xu, Bing, 2022. "Implementation of penalized survival models in churn prediction of vehicle insurance," Journal of Business Research, Elsevier, vol. 153(C), pages 162-171.
    2. Ibrahim Al-Shourbaji & Na Helian & Yi Sun & Samah Alshathri & Mohamed Abd Elaziz, 2022. "Boosting Ant Colony Optimization with Reptile Search Algorithm for Churn Prediction," Mathematics, MDPI, vol. 10(7), pages 1-21, March.
    3. Yunjie Liu & Mu Shengdong & Gu Jijian & Nadia Nedjah, 2022. "Intelligent Prediction of Customer Churn with a Fused Attentional Deep Learning Model," Mathematics, MDPI, vol. 10(24), pages 1-16, December.

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